Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection

Abstract

Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.

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Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

“…Strawberries are susceptible to many insects, mites, pests, and microorganisms (bacteria, fungi, and viruses) that regularly cause reductions in total and marketable yield [148,149].…”

“…For example, Park et al [151] applied a CNN to classify healthy and diseased strawberry using RGB images taken by a smart phone, with 89.7% accuracy. Chang et al [149] extracted 40 textural indices from high-resolution RGB images and compared the performance of three supervised learning classifiers, ANNs, SVMs, and K-nearest neighbors (KNNs), in detecting the strawberry powdery mildew disease. The overall classification accuracy was 93.8% and 78.80% for the ANN and KNN classifiers, respectively.…”